Data Science with Python

Course Title: Data Science with Python

Course Duration: 50 hours

Course Objective:

This course aims to provide students with the necessary skills and tools to analyze real-world data, build models, and generate insights using Python. The course covers Python fundamentals, data manipulation, statistical analysis, machine learning, data visualization, and practical data science project work.

Week 1: Introduction to Data Science & Python

• Topics:
  • Overview of Data Science
  • The Data Science Process
  • Python Fundamentals for Data Science (variables, data types, loops, functions)
  • Introduction to Jupyter Notebooks
• Assignments:
  • Basic Python exercises (e.g., loops, functions, lists)

Week 2: Python Libraries for Data Science

• Topics:
  • Introduction to Python Libraries for Data Science
    • NumPy for numerical computing
    • Pandas for data manipulation
  • Basic Data Structures (Arrays, DataFrames, Series)
• Assignments:
  • NumPy and Pandas exercises (e.g., matrix operations, basic data manipulation)

Week 3: Data Wrangling and Cleaning

• Topics:
  • Importing and Exporting Data (CSV, Excel, SQL)
  • Handling Missing Data
  • Data Transformation and Cleaning
  • Exploratory Data Analysis (EDA)
• Assignments:
  • Cleaning and transforming real-world datasets

Week 4: Data Visualization

• Topics:
  • Matplotlib and Seaborn for Visualization
  • Creating Bar Charts, Line Plots, Scatter Plots, and Histograms
  • Advanced Plots: Heatmaps, Pairplots, Boxplots
  • Customizing Plots (color schemes, annotations)
• Assignments:
  • Visualization exercises using real datasets

Week 5: Probability and Statistics for Data Science

• Topics:
  • Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
  • Probability Distributions (Normal, Binomial)
  • Hypothesis Testing (Z-test, T-test)
  • Introduction to Statistical Inference
• Assignments:
  • Statistical analysis of datasets

Week 6: Introduction to Machine Learning

• Topics:
  • Supervised vs. Unsupervised Learning
  • Introduction to Scikit-Learn
  • Simple Linear Regression
  • Model Evaluation (Train/Test Split, RMSE, MAE)
• Assignments:
  • Implementing a linear regression model on a dataset

Week 7: Classification Algorithms

• Topics:
  • Logistic Regression
  • Decision Trees and Random Forests
  • Evaluating Classification Models (Accuracy, Precision, Recall, F1 Score)
• Assignments:
  • Classification tasks using real datasets

Week 8: Clustering and Unsupervised Learning

• Topics:
  • Introduction to Clustering
  • K-Means Clustering
  • Dimensionality Reduction (PCA)
  • Hierarchical Clustering
• Assignments:
  • Applying clustering algorithms to datasets

Week 9: Advanced Machine Learning Techniques

• Topics:
  • Support Vector Machines (SVM)
  • Gradient Boosting (XGBoost, AdaBoost)
  • Hyperparameter Tuning (GridSearch, RandomizedSearch)
• Assignments:
  • Advanced machine learning models on real datasets

Week 10: Time Series Analysis

• Topics:
  • Introduction to Time Series Data
  • ARIMA Models
  • Seasonality and Trend Analysis
  • Forecasting
• Assignments:
  • Time series forecasting on historical data

Week 11: Deep Learning (Optional)

• Topics:
  • Introduction to Neural Networks
  • Basics of TensorFlow/Keras
  • Building a Simple Neural Network
  • Evaluating Neural Networks
• Assignments:
  • Building a basic neural network model

Week 12: Capstone Project

• Topics:
  • End-to-End Data Science Project
  • Problem Framing, Data Collection, and Cleaning
  • Model Building, Evaluation, and Reporting
  • Presentation of Results
• Assignments:
  • Complete a full data science project (including documentation and presentation)

Grading:

• Weekly Assignments: 40%
• Midterm Project: 20%
• Capstone Project: 30%
• Participation & Quizzes: 10%

Recommended Texts and Resources:

• Books:
  • Python for Data Analysis by Wes McKinney
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
• Online Resources:
  • Kaggle Datasets and Competitions
  • Scikit-Learn Documentation
  • Pandas and NumPy Documentation